Abstract
Unarguably, with the unparalleled emergence of metamorphic utilization of mobile computing gadgets combining with the social networks. Hefty and massive amount of data are unprecedentedly generated within a second. Search engines host diversified streams of information have created unprecedented scattered data. Hence, effective management and the capability to process large-scale data pose an interesting but critical challenge for contemporary business organizations. Substantively, customers are expanding their online footprints extensively, which makes it hard to extract data value through data collection and data mining. Due to the distributed databases embedded based on heterogeneous platforms, business organizations are facing problematic challenges. It becomes urgent research issues to efficiently and effectively conducting data mining mechanisms with respect to massive amount of data to meet the organizational strategic objectives. Evidently, Big Data era has witnessed the rigorous challenges concerning data transferring, integration, and data-processing technologies. Proverbially, the commonly known Intelligent Agents (IAs), as the autonomous entities to direct its actions towards diverse goals in order to satisfy the implicit requirements for high-speed data integration as well as cooperation mechanisms among different heterogeneous databases. Literally, a Multi-Agent System (MAS) can deal with the flexible communication and cooperation among distributed intelligent agents as an information processor. This paper will introduce multi-agent systems and their applications from data mining aspect, followed by the value of data mining from Customer Relationship Management (CRM) aspect. At last, we propose a three-step data-mining model, which can help business organizations to dig out potential value to manage CRM optimally including using K-means to cluster massive data. In addition, we generalize data to focus on relevant attributes via using information gained and information entropy calculation method to make decision trees for extracting potential valuable knowledge purpose.
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References
Bittencourt, I., Costa, E., Silva, M., Soares, E.: A Computational Model for Developing Semantic Web-based Educational Systems. Knowledge Based Systems 22, 302–315 (2009)
Bueren, A., Schierholz R., Kolbe L., Brenner, W.: Customer Knowledge Management: Improving Performance of Customer Relationship Management with Knowledge Management. In: Proceedings of the 37th IEEE Hawaii International Conference on System Sciences. IEEE Computer Society Press, Big Island, HI
Chen, D., Vachharajani, N., Hundt, R., Li, X., Eranian, S., Chen, W., Zheng, W.: Taming Hardware Event Samples for Precise and Versatile Feedback Directed Optimizations. IEEE Transactions on Computers 62(2), 376–389 (2013)
Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized QoS-Aware Web Service Recommendation and Visualization. IEEE Transactions on Services Computing 6(1), 35–47 (2013)
Fung, G., Mangasarian, L.O.: ‘Proximal Support Vector Machine Classifiers’ Knowledge Discovery and Data Mining, pp. 77–86, New York, NY, USA (2001)
Hsu, C.H., Hsu, C.G., Chen, S.C., Chen, T.L.: Message Transmission Techniques for Low Traffic P2P Services. International Journal of Communication Systems 22(9), 1105–1122 (2009)
Hsu, C., Chen, Y., Kang, H.: Performance-Effective and Low-Complexity Redundant Reader Detection in Wireless RFID Networks. EURASIP Journal on Wireless Communications and Networking 1–9 (2008)
Kuoa, R.J., Ana, Y.L., Wanga, H.S., Chungbi, W.J.: Integration of Self-Organizing Feature Maps Neural Network and Genetic K-means Algorithm for Market Segmentation. Expert System 313–324 (2006)
Romdhane, L.B., Nadia, F., Ayeb, B.: Building Customer Models From Business Data: An Automatic Approach Based on Fuzzy Clustering and Machine Learning. International Journal of computational intelligence and application. 8(4), 445–465 (2009)
Guo, J., Xu, M.: The Implementation of Enterprise CRM Based on Big Data Mining Technologies (Chinese). http://www.chinadmd.com/file/uei3uaosocwevsetuziuocxr_1.html
Giudici, P., Passerone, G.: Data Mining of Association Structures to Model Consumer Behavior. Computer Statistics Data Analysis 533–541 (2002)
Mitra, S., Pal, S.K., Mitra, P.: Data Mining in Soft Computing Framework: A Survey. IEEE Trans. Neural Networks 3–14 (2002)
Soukakos, P.I., Georgopoulos, N.B., Pekka Economou, V.: Interrelated Frame-Works Proposed for Mapping and Performance Measurement of Customer Relationship Management Strategies. International Journal of Knowledge and Learning 299–315 (2007)
Thomas, A.M., Shah, H., Moore, P., Rayson, P.: E-Education 3.0: Challenges and Opportunities for the Future of iCampuses. In: International Conference on Digital Object Identifier, pp. 953–958 (2012)
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Xu, L., Chu, HC. (2015). The Cooperation Mechanism of Multi-agent Systems with Respect to Big Data from Customer Relationship Management Aspect. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_54
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DOI: https://doi.org/10.1007/978-3-319-15702-3_54
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